Mean Shape Models for Polyp Detection in CT Colonography

Gastric polyp detection module based on improved attentional feature fusion

Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected.

Landmark-free Shape Analysis of the Human Duodenum. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083606 DOI: 10.1109/embc40787.2023.10340464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The primary function of the duodenum is to undertake chemical digestion by ensuring that the partially digested food received from the stomach is well-mixed with the enzymes and chemicals secreted into it. However, little is known about the anatomical variations in the shape of the duodenum within humans, and thus the effect of duodenum shape on the flow and mixing occurring within the lumen has not been studied. In this work, a methodology for analyzing shape variations in the normal duodenal anatomy has been developed and applied to a publicly available dataset of abdominal CT images. This method does not require the placement of landmarks as it is based on the underlying tubular 'C' shape of the duodenum. The average duodenal length and radius of this dataset (consisting of 34 subjects) were 212.8 ± 38 mm and 10.8 ± 2.5 mm respectively. A Principal Component Analysis (PCA) was conducted on a sample of 34 duodenums after normalizing their lengths and the first five principal components were found to contribute to 82 % of the total variation. The first shape component (accounting for 42 % of overall variation) consisted of variations in the radius along the duodenum with no deformations normal to the central plane, and the subsequent shape modes consisted of twists in the centerline either in and out of the central plane, and radial variations at either the inlet or outlet. This is the first study to analyze shape variations in the human duodenum and the results can be combined with flow modeling to analyze the effect of shape on the flow and mixing occurring within the duodenum.Clinical relevance- The methods developed in this study can be used by clinicians to diagnose abnormalities in an individual's duodenum shape.

A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans

PURPOSE

Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives.

METHODS

The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier.

RESULTS

Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps ([Formula: see text]6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset.

CONCLUSIONS

To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.